Deep Learning Model Compression and Hardware Acceleration for High-Performance Foreign Material Detection on Poultry Meat Using NIR Hyperspectral Imaging by Zirak Khan, Seung-Chul Yoon, Suchendra M. Bhandarkar

“…Ensuring the safety and quality of poultry products requires efficient detection and removal of foreign materials during processing. Hyperspectral imaging (HSI) offers a non-invasive mechanism to capture detailed spatial and spectral information, enabling the discrimination of different types of contaminants from poultry muscle and non-muscle external tissues. …”
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SAGNet: Small-Target Attention Guided Network for Urban Remote Sensing Image Real-Time Segmentation by Shasha Ren, Xiaodong Zhang

“…Existing image segmentation methods based on feature fusion and attention mechanisms effectively enhance the features of large targets in URS images, but introduce noise, which leads to blurring of small target features in the feature space. …”
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Remote sensing monitoring of wheat aphids by combining HJ satellite images with least squares twin support vector machine model by HU Gensheng, WU Wentian, LUO Juhua, HUANG Wenjiang, LIANG Dong, HUANG Linsheng

“…Computational complexity of LSTSVM is reduced by using the least squares algorithm to transform inequality constraints into equality constraints.Experimental results showed that: the overall monitoring accuracy of the LSTSVM model was 86.4% and the Kappa coefficient was 0.71; the traditional SVM model was 77.3% and 0.52; the Fisher linear discriminant analysis (FLDA) model was 77.3% and 0.54; and the learning vector quantization (LVQ) neural network model was 72.7% and 0.39. …”
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DeepBiteNet: A Lightweight Ensemble Framework for Multiclass Bug Bite Classification Using Image-Based Deep Learning by Doston Khasanov, Halimjon Khujamatov, Muksimova Shakhnoza, Mirjamol Abdullaev, Temur Toshtemirov, Shahzoda Anarova, Cheolwon Lee, Heung-Seok Jeon

“…<b>Methods</b>: For this work, we introduce DeepBiteNet, a new ensemble-based deep learning model designed to perform robust multiclass classification of insect bites from RGB images. Our model aggregates three semantically diverse convolutional neural networks—DenseNet121, EfficientNet-B0, and MobileNetV3-Small—using a stacked meta-classifier designed to aggregate their predicted outcomes into an integrated, discriminatively strong output. …”
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Optimization of Complex Cancer Morphology Detection Using the SIVQ Pattern Recognition Algorithm by Jason Hipp, Steven Christopher Smith, Jerome Cheng, Scott Arthur Tomlins, James Monaco, Anant Madabhushi, Lakshmi Priya Kunju, Ulysses J. Balis

“…In a number of cases in surgical pathology, morphometric quantitation is already performed manually or semi-quantitatively, with this effort contributing to diagnostic workup. Digital whole slide imaging, coupled with emerging image analysis algorithms, offers great promise as an adjunctive tool for the surgical pathologist in areas of screening, quality assurance, consistency, and quantitation. …”
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A lightweight intelligent compression method for fast Sea Level Anomaly data transmission. by Xiaodong Ma, Xiang Wan, Lei Zhang, Dong Wang, Zeyuan Dai

“…Furthermore, parametric pruning and adaptive quantization strategies are embedded to optimize memory efficiency for shipborne hardware constraints. …”
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Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma. by José Fernando García Molina, Lei Zheng, Metin Sertdemir, Dietmar J Dinter, Stefan Schönberg, Matthias Rädle

“…Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. …”
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GenVFNet: Generating Visual Field From Optical Coherence Tomography Angiography by Conditional Generative Adversarial Networks by Anita Manassakorn, Supatana Auethavekiat, Vera Sa-Ing, Sunee Chansangpetch, Kitiya Ratanawongphaibul, Nopphawan Uramphorn, Visanee Tantisevi

“…To conform with the five severity levels in clinical diagnosis, we quantized the generated VF (GenRawVF) from GenVFNet into five levels and named the quantized image GenVF. …”
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